function [vL,vR,vLo,vRo]=RFNN_cal_v5(dg,sig,do,sio)
    aph=0.6;
    b=0.098;
    S=[dg,do,sig,sio];
%     dt=0.1;
    % 初始化静态变量
%     a=[0.74,0.66,0.71,0.28,0.1,0.69,0.95,0.44,0.77,0.19,0.45,0.71,0.28,0.66,0.12,0.96;
%             0.39,0.17,0.03,0.05,0.82,0.32,0.03,0.38,0.8,0.49,0.65,0.75,0.68,0.16,0.5,0.34];
    a=[0.11,0.14,0.17,0.62,0.57,0.05,0.93,0.73,0.74,0.06,0.86,0.93,0.98,0.86,0.79,0.51;
                0.18,0.40,0.13,0.03,0.94,0.30,0.30,0.33,0.47,0.65,0.03,0.84,0.56,0.85,0.35,0.45];
    %神经网络部分计算输出
    W=a*sum(S(:));
    [Outputs,Fnk]=RFNNpro_v(S,W);
    vR=Outputs(1)/(1+aph*abs(Outputs(1)));
    vL=Outputs(2)/(1+aph*abs(Outputs(2)));
    ran_error=-1+2*rand();
    vLo=vL+0.15*ran_error;
    ran_error=-1+2*rand();
    vRo=vR+0.15*ran_error;
    if(vLo>1.47)
        vLo=1.47;
    elseif(vLo<0.22)
        vLo=0;
    end
    if(vRo>1.47)
        vRo=1.47;
    elseif(vRo<0.22)
        vRo=0;
    end
end